Book Image

Python Algorithmic Trading Cookbook

By : Pushpak Dagade
Book Image

Python Algorithmic Trading Cookbook

By: Pushpak Dagade

Overview of this book

If you want to find out how you can build a solid foundation in algorithmic trading using Python, this cookbook is here to help. Starting by setting up the Python environment for trading and connectivity with brokers, you’ll then learn the important aspects of financial markets. As you progress, you’ll learn to fetch financial instruments, query and calculate various types of candles and historical data, and finally, compute and plot technical indicators. Next, you’ll learn how to place various types of orders, such as regular, bracket, and cover orders, and understand their state transitions. Later chapters will cover backtesting, paper trading, and finally real trading for the algorithmic strategies that you've created. You’ll even understand how to automate trading and find the right strategy for making effective decisions that would otherwise be impossible for human traders. By the end of this book, you’ll be able to use Python libraries to conduct key tasks in the algorithmic trading ecosystem. Note: For demonstration, we're using Zerodha, an Indian Stock Market broker. If you're not an Indian resident, you won't be able to use Zerodha and therefore will not be able to test the examples directly. However, you can take inspiration from the book and apply the concepts across your preferred stock market broker of choice.
Table of Contents (16 chapters)

Creating a DataFrame from other formats

In this recipe, you will create DataFrame objects from other formats, such as .csv files, .json strings, and pickle files. A .csv file created using a spreadsheet application, valid JSON data received over web APIs, or valid pickle objects received over sockets can all be processed further using Python by converting them to DataFrame objects.

Loading pickled data received from untrusted sources can be unsafe. Please use read_pickle() with caution. You can find more details here: If you are using this function on the pickle file created in the previous recipe, it is perfectly safe to use read_pickle().

Getting ready

Make sure you have followed the previous recipe before starting this recipe.

How to do it…

Execute the following steps for this recipe:

  1. Create a DataFrame object by reading a CSV file:
>>> pandas.read_csv('dataframe.csv')

We get the following output: